A Deep Dive into the Impact of Legislation on Humanitarian Aid Worker Deaths

Code 
import pandas as pd 
import matplotlib.pyplot as plt
import plotly.graph_objects as go

df = pd.read_csv("../DataSet/security_incidents.csv")
#df.head()

Breakdown of Aid Workers death by Year

Code 
# Create interactive line chart
fig = go.Figure()
# Grouping the data by Year and summing the Total killed column to get total deaths per year
annual_deaths = df.groupby('Year')['Total killed'].sum().reset_index()

fig.add_trace(go.Scatter(
    x=annual_deaths['Year'],
    y=annual_deaths['Total killed'],
    mode='lines+markers',
    name='Total Deaths',
    line=dict(color='#E44E50', width=3),
    marker=dict(size=8, color='white', line=dict(width=2, color='#E44E50')),
    hovertemplate='Year: %{x}<br>Deaths: %{y}<extra></extra>'
))

# Customize layout
fig.update_layout(
    title='Total Humanitarian Aid Worker Deaths (1997–2025)',
    xaxis_title='Year',
    yaxis_title='Total Deaths',
    template='plotly_white',
    font=dict(size=14),
    hovermode='x unified',
    width=650,
    height=400,
    paper_bgcolor="#f9f9f9",  # off white background]
    plot_bgcolor="#f9f9f9"
)

fig.show()

2023–2024 saw a dramatic spike in humanitarian aid worker deaths, reaching the highest point in the observed period. This surge is largely attributed to the wars in Gaza and Sudan. While the overall trend shows a general increase over time, there are noticeable fluctuations, with some years experiencing significant drops or local peaks. To better understand the dynamics of these changes, we will specifically examine the years 2003, 2014, 2016 and 2024, each of which corresponds to notable variations in the data.

Top Ten Countries with the Highest Death in Aid Workers

Code 
import seaborn as sns
# Prepare data
grouped_by_country = df.groupby("Country")["Total killed"].sum().sort_values(ascending=False)
top_10 = grouped_by_country.head(10).reset_index()

# Set theme
sns.set_theme(style="whitegrid", rc={
    "axes.facecolor": "#f9f9f9",
    "figure.facecolor": "#f9f9f9"
})

# Create plot
plt.figure(figsize=(8.5, 6))
sns.barplot(
    data=top_10,
    x="Total killed",
    y="Country",
    hue="Country",  # this links the palette to the bar labels
    palette="Reds_r",
    dodge=False,
    legend=False
)

# Titles and labels
plt.title('Top 10 Countries by Total Aid Worker Deaths', fontsize=16, weight='bold')
plt.xlabel('Total Deaths')
plt.ylabel('Country')
# Remove borders
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.show()

From the graph above, we can see that Afghanistan has the highest number of aid worker deaths, followed by Palestine, Syria, and South Sudan. These are regions with a high concentration of conflict and war. This trend reflects major geopolitical events such as post-9/11 warfare and the recent war in Gaza. To better understand the fluctuations in aid worker deaths, we will focus specifically on what was happening in these countries during the years 2003, 2014, 2016, and 2024.

Key Legislation Passed to Protect Aid Workers (2003, 2014, 2016, 2024)

Code 
import matplotlib.cm as cm
focus_years = [2003, 2014, 2016, 2024]
filtered_df = df[df["Year"].isin(focus_years)]
deaths_by_year = filtered_df.groupby("Year")["Total killed"].sum().reset_index()

# Vibrant red shades, in order of the 4 bars
colors = ['#ff9999', '#ff6666', '#ff3333', '#cc0000']  # Nice, clean red tones

# Plot
fig, ax = plt.subplots(figsize=(8, 5), facecolor="#f9f9f9")
ax.set_facecolor("#f9f9f9")
plt.bar(deaths_by_year["Year"].astype(str), deaths_by_year["Total killed"], color=colors)
plt.xlabel("Year")
plt.ylabel("Total Aid Worker Deaths")
plt.title("Total Aid Worker Deaths in Focus Years")
plt.grid(axis='y', linestyle='--', alpha=0.7)
# Remove borders
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()

Year Resolution Focus
2003 UNSC Resolution 1502 Protection of humanitarian workers
2014 UNSC Resolution 2175 Condemned attacks on aid workers
2016 UNSC Resolution 2286 Medical and humanitarian personnel
2024 UNSC Resolution 2730 Aid worker protection accountability

Can Laws Protect Aid Workers from Being Targeted?

Evidence of Impact: T-Test Results for Legislation

Code 
# Filter data from 1998 to 2008 for comparison around in 2003
window_df = df[(df["Year"] >= 1998) & (df["Year"] <= 2008)]

# Aggregate total deaths per year
deaths_window = window_df.groupby("Year")["Total killed"].sum().reset_index()

# Add a column indicating whether the year is before or after the legislation
deaths_window["Period"] = deaths_window["Year"].apply(lambda x: "Before" if x < 2003 else "After")

# Calculate average deaths before and after
avg_deaths = deaths_window.groupby("Period")["Total killed"].mean().reset_index().rename(columns={"Total killed": "Average Deaths"})

# T-test to check statistical significance
from scipy.stats import ttest_ind

before_deaths = deaths_window[deaths_window["Period"] == "Before"]["Total killed"]
after_deaths = deaths_window[deaths_window["Period"] == "After"]["Total killed"]
t_stat, p_value = ttest_ind(before_deaths, after_deaths, equal_var=False)

avg_deaths, t_stat, p_value
(   Period  Average Deaths
 0   After       83.666667
 1  Before       38.000000,
 -3.7532133735559468,
 0.007239023810519176)

Running a t-test, we find that the only statistically significant p-value occurred in 2003. This indicates a significant increase in aid worker deaths following the adoption of UNSC Resolution 1502. Rather than reducing fatalities, the average number of deaths rose from 38.0 before to 83.7 after 2003 (t = -3.75, p = 0.0072). This suggests that the legislation did not lead to improved protection for humanitarian aid workers during that period. For the other focus years, 2014, 2016, and 2024, the p-values were not statistically significant. This means that any differences in death counts before and after the respective legislation in those years could be due to random variation, rather than the result of effective policy change.

Code 
# Set style
sns.set_theme(style="whitegrid", context="talk")
sns.set_palette("deep") 
# Create plot
fig, ax = plt.subplots(figsize=(8, 5), facecolor="#f9f9f9")
ax.set_facecolor("#f9f9f9")
# Line plot
plt.plot(deaths_window["Year"], deaths_window["Total killed"], 
         marker='o', linestyle='-', linewidth=2, 
         markersize=6, color='#10EFD9', markerfacecolor='white', markeredgewidth=2)
# Vertical line for 2003 legislation
plt.axvline(x=2003, color='#EF1026', linestyle='--', linewidth=2, label="UNSC Resolution 1502 (2003)")

# Titles and labels
plt.title("Aid Worker Deaths (1998–2008) with 2003 Legislation Marker", fontsize=16, weight='bold')
plt.xlabel("Year", fontsize=12)
plt.ylabel("Total Aid Worker Deaths", fontsize=12)

# Legend styling
plt.legend(frameon=True, framealpha=0.9, facecolor='white', loc="upper left")

# Grid, layout, and display
plt.grid(True, linestyle='--', alpha=0.5)
plt.xticks(deaths_window["Year"], rotation=45)
# Remove borders
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.show()

As shown in the graph above, aid worker deaths after 2003 are significantly higher than those before 2003. This sharp increase highlights that legislation alone is not sufficient to prevent aid worker fatalities.

Digging Deeper: Which Aid Groups Are Most at Risk?

Code 
import plotly.express as px
# Define organization-related columns
org_columns = ['UN', 'INGO', 'NNGO', 'ICRC', 'Other']
# Melt the dataset to long format
org_long = df[["Year", "Total killed"] + org_columns].melt(
    id_vars=["Year", "Total killed"],
    value_vars=org_columns,
    var_name="Organization",
    value_name="Involved"
)

# Filter only incidents where the org was involved
org_long = org_long[org_long["Involved"] == 1]

# Group by Year and Organization
org_grouped = org_long.groupby(["Year", "Organization"])["Total killed"].sum().reset_index()

# Pivot for area chart
org_pivot = org_grouped.pivot(index="Year", columns="Organization", values="Total killed").fillna(0)

# Compute total deaths across all orgs per year
org_total_year = org_grouped.groupby("Year")["Total killed"].sum().reset_index().rename(columns={"Total killed": "Total All Orgs"})

# Merge to get percent
org_percent = org_grouped.merge(org_total_year, on="Year")
org_percent["Percent"] = (org_percent["Total killed"] / org_percent["Total All Orgs"]) * 100

# Define custom colors for each organization
custom_colors = {
    "ICRC": "#E21D51",    
    "INGO": "#B4E21D",   
    "Other": "#1DE2AE",   
    "UN": "#4B1DE2",      
    "NNGO": "#E88817"      
}

# Plot line chart with updated color map
fig = px.line(
    org_percent,
    x="Year",
    y="Percent",
    color="Organization",
    markers=True,
    title="Percent of Aid Worker Deaths by Organization Type Each Year",
    labels={"Percent": "Percent of Total Deaths"},
    color_discrete_map=custom_colors
)

# Set background to white
fig.update_layout(
    plot_bgcolor='#f9f9f9',
    paper_bgcolor='#f9f9f9',
    xaxis=dict(showgrid=True, gridcolor='lightgray'),
    yaxis=dict(showgrid=True, gridcolor='lightgray'),
    legend_title="Organization",
    hovermode="x unified"
)

fig.update_traces(line=dict(width=2))
fig.show()
Code 
# Group by organization to compute total deaths
org_grouped = org_long.groupby(["Year", "Organization"])["Total killed"].sum().reset_index()
total_deaths_by_org = org_grouped.groupby("Organization")["Total killed"].sum().reset_index()
total_deaths_by_org = total_deaths_by_org.sort_values("Total killed", ascending=True)
# Define custom reds
custom_reds = ["#ffcccc", "#ff6666", "#ff3333", "#cc0000", "#990000"]
# Create the pretty horizontal bar chart
sns.set_theme(style="whitegrid")
fig, ax = plt.subplots(figsize=(8, 5), facecolor="#f9f9f9")
ax.set_facecolor("#f9f9f9")
barplot = sns.barplot(
    data=total_deaths_by_org,
    x="Total killed",
    y="Organization",
    hue="Organization", 
     palette=custom_reds,
    legend=False  
)
# Add value labels to each bar
for index, value in enumerate(total_deaths_by_org["Total killed"]):
    plt.text(value + 2, index, f"{int(value)}", va='center', fontsize=10)

# Title and axis labels
plt.title("Total Aid Worker Deaths (1997-2025) by Organization Type", fontsize=16)
plt.xlabel("Total Deaths")
plt.ylabel("Organization")

# Remove borders and apply layout
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.show()

Conclusion

The shifting distribution of aid worker deaths over the past two decades reveals a critical gap in the effectiveness of international protections. While UNSC resolutions and other global efforts aim to safeguard humanitarian actors, the data shows that international legislation tends to benefit international organizations, like the UN and INGOs, more directly. These groups often have the diplomatic backing, security infrastructure, and global visibility to leverage such protections. In contrast, National NGOs (NNGOs), whose staff are typically local and operating on the front lines, now account for an increasingly larger share of total deaths, particularly in recent years. This raises a troubling question: if the most vulnerable actors are not seeing the benefits of international legislation, then who is the system truly protecting? The growing proportion of deaths among local humanitarian staff suggests that while laws may exist on paper, their implementation and enforcement remain uneven, leaving national actors exposed to disproportionate risk. There is an urgent need for legislation that not only exists but equitably protects all humanitarian personnel, especially those most at risk. One way forward is to support community-driven protection strategies, rather than relying solely on top-down frameworks from the UN. Another is to give national aid workers a seat at the table in the creation of future legislation intended to protect humanitarian personnel. Overall, while international legislation has offered some protection to international aid workers, it has too often left national staff behind.